Compact aero-thermo model based tip clearance management

ABSTRACT

Systems and methods for controlling a fluid based engineering system are disclosed. The systems and methods may include a model processor for generating a model output, the model processor including a set state module for setting dynamic states of the model processor, the dynamic states input to an open loop model based on the model operating mode, wherein the open loop model generates a current state model as a function of the dynamic states and the model input, wherein a constraint on the current state model is based a series of cycle synthesis modules, each member of the series of cycle synthesis modules modeling a component of a cycle of the control system and including a series of utilities, the utilities are based on mathematical abstractions of physical properties associated with the component, the series of cycle synthesis modules including a rotary apparatus module which estimates a tip clearance between the rotor and the rotor case. The model processor may further include an estimate state module for determining an estimated state of the model based on a prior state model output and the current state model of the open loop model.

This application is a US National Stage under 35 USC § 371 ofInternational Patent Application No. PCT/US14/27848 filed on Mar. 14,2014, and claims priority under 35 USC § 119(e) to U.S. ProvisionalPatent Application Ser. No. 61/800,440 filed on Mar. 15, 2013.

TECHNICAL FIELD OF THE DISCLOSURE

The present disclosure relates to the design and control of engineeringsystems, and more particularly, to design and control of fluid-basedengineering systems.

BACKGROUND OF THE DISCLOSURE

Fluid-based engineering systems are widely used and may include gasturbine engines for aviation and power generation, HVAC&R (heating,ventilation, air-conditioning and refrigeration), fuel cells, and other,more generalized fluid processing systems for hydrocarbon extraction,materials processing, and manufacture. These systems may contain any orall of the following components: turbo-machinery, fuel cell stacks,electric motors, pipes, ducts, valves, mixers, nozzles, heat exchangers,gears, chemical apparatuses and other devices for generating ormodifying a fluid flow.

Each of these applications places different operational demands on theengineering control system. In gas turbine engine applications, forexample, the relevant cycle is typically a Brayton turbine or firstEricsson cycle, and the basic thermodynamic parameters (or processvariables) are the pressure, temperature and flow rate of the workingfluid at the inlet, compressor, combustor, turbine, and exhaust. Theparameters may be related to the overall thrust, rotational energy, orother measure of power output. In order to precisely control this outputwhile maintaining safe, reliable and efficient engine operation, theengineering control system must be fast, accurate, robust, and providereal-time control capability across all required performance levels.While the relevant process variables vary depending on the system typeand configuration, the need for precise, efficient and reliableengineering control remains the same, as do the economic constraints onoverall cost and operational/maintenance requirements.

Further, because direct measurements of system parameters controlled maynot be possible (due to undeveloped technology, prohibitive cost,unreliable equipment, etc.), the control system may require real timeestimation of system parameters. In particular, such a control systemmay wish to monitor parameters associated with tip clearance in a rotorbased apparatus. System parameters may be mathematical abstractions ofengineering systems and/or process for a given set of measured inputsused as control feedback.

In the past, control systems for such fluid-based engineering systemsrelied on piecewise linear state variable representations. These controlsystems, by their nature, were limited to relatively simple non-linearsystems. Another approach used in the past relies on semi-empiricalrelationships that tie important system parameters to control sensors;the drawback of such a system is that it may lack accuracy and isexpensive due to the additional hardware required for implementation.Other attempts have been made to deploy stationary simulations in aretail environment; however, by their nature, these models are large,use iterative solvers, have high maintenance cost and lack robustnesscritical in a real time environment.

A known approach to modern fluid-based engineering system control is theuse of component level physics based non-iterative mathematicalabstractions of fluid-based engineering systems. These mathematicalabstractions are conceptualized in a software environment specific tothe applied fluid-based engineering system. Such example systems andmethods for engineering system control are further detailed in U.S. Pat.No. 8,090,456 (“System and method for design and control of engineeringsystems utilizing component-level dynamic mathematical model”), which ishereby incorporated by reference.

A need exists for an engine parameter on-board synthesis (EPOS) in thereal-time control system of a fluid based engineering system that mayovercome the computational inefficiencies of prior EPOS models. Inparticular, control systems for fluid-based engineering systems thatcontrol and/or monitor tip clearance of components of fluid-basedengineering are needed. Thusly, an EPOS providing said functionalitythat may overcome the computational inefficiencies of prior EPOS modelsis needed.

SUMMARY OF THE DISCLOSURE

In accordance with an aspect of the disclosure, a control system isdisclosed. The control system may include an actuator for positioning acontrol device, the control device comprising a rotary apparatusincluding a rotor and a rotor case, wherein the actuator positions thecontrol surface in order to control a model state. The system mayinclude a control law for directing the actuator as a function of amodel output. The control system may include a model processor forgenerating the model output, the model processor including an inputobject for processing model input and setting a model operating mode, aset state module for setting dynamic states of the model processor, thedynamic states input to an open loop model based on the model operatingmode, wherein the open loop model generates a current state model as afunction of the dynamic states and the model input, wherein a constrainton the current state model is based a series of cycle synthesis modules,each member of the series of cycle synthesis modules modeling acomponent of a cycle of the control device and comprising a series ofutilities, the utilities are based on mathematical abstractions ofphysical properties associated with the component. The series of cyclesynthesis modules may include a rotary apparatus module which estimatesa tip clearance between the rotor and the rotor case. The modelprocessor may further include an estimate state module for determiningan estimated state of the model based on a prior state model output andthe current state model of the open loop model and an output object forprocessing the estimated state of the model to determine the modeloutput.

In a refinement, the rotary apparatus module may estimate temperatureand rate of temperature change values for the rotor and temperature andrate of temperature change values for the rotor case.

In a further refinement, the rotary apparatus module may estimate aradial growth of the rotor based on the temperature and rate oftemperature change values for the rotor and may estimate a radial growthof the rotor case based on the temperature and rate of temperaturechange values for the rotor case.

In a further refinement, the rotary apparatus module may estimate thetip clearance between the rotor and the rotor case based on theestimated radial growth of the rotor and the estimated radial growth ofthe rotor case.

In a refinement, the control law may direct the actuators to control aflow in a gas path of the rotary apparatus based on the tip clearance.

In a further refinement, the control law may direct the actuator toregulate a coolant flow onto the rotor case to maintain a desired tipclearance between the rotor and the rotor case.

In a refinement, the rotary apparatus may include at least one of acompressor element or a turbine element.

In a refinement, the estimated tip clearance is based on one or more ofmaterial temperature estimations, rotary speed calculations, gas pathpressure estimations, and mechanical growth estimations.

In a refinement, the control device is a gas turbine engine.

In accordance with another aspect of the disclosure, a method forcontrolling a control device is disclosed. The control device mayinclude a rotary apparatus including a rotor and a rotor case. Themethod may include generating a model output using a model processor.The control system may include a model processor for generating themodel output, the model processor including an input object forprocessing model input and setting a model operating mode, a set statemodule for setting dynamic states of the model processor, the dynamicstates input to an open loop model based on the model operating mode,wherein the open loop model generates a current state model as afunction of the dynamic states and the model input, wherein a constrainton the current state model is based a series of cycle synthesis modules,each member of the series of cycle synthesis modules modeling acomponent of a cycle of the control device and comprising a series ofutilities, the utilities are based on mathematical abstractions ofphysical properties associated with the component. The series of cyclesynthesis modules may include a rotary apparatus module which estimatesa tip clearance between the rotor and the rotor case. The modelprocessor may further include an estimate state module for determiningan estimated state of the model based on a prior state model output andthe current state model of the open loop model and an output object forprocessing the estimated state of the model to determine the modeloutput. The method may further include directing an actuator associatedwith the control device as a function of a model output using a controllaw and positioning the control device using the actuator, wherein theactuator positions the control device in order to control the modelstate.

In a refinement, the rotary apparatus module may estimate temperatureand rate of temperature change values for the rotor and temperature andrate of temperature change values for the rotor case.

In a further refinement, the rotary apparatus module may estimate aradial growth of the rotor based on the temperature and rate oftemperature change values for the rotor and may estimate a radial growthof the rotor case based on the temperature and rate of temperaturechange values for the rotor case.

In a further refinement, the rotary apparatus module may estimate thetip clearance between the rotor and the rotor case based on theestimated radial growth of the rotor and the estimated radial growth ofthe rotor case.

In a refinement, the control device may be a gas turbine engine and theone or more cycle synthesis modules may be based on one or moremathematical abstractions of physical processes associated withcomponents of a thermodynamic cycle of the gas turbine engine.

In a refinement, further comprising directing the actuator, using thecontrol law, to regulate a coolant flow onto the rotor case to maintaina desired tip clearance between the rotor and the rotor case based onthe estimated tip clearance.

In accordance with another aspect of the disclosure, a gas turbineengine is disclosed. The gas turbine engine may include a fan, acompressor section downstream of the fan, a combustor section downstreamof the compressor section. The turbine section may include a turbinerotor and a turbine case. Further, the gas turbine engine may include anactuator for positioning the gas turbine engine comprising a controlsurface, wherein the actuator positions the control surface in order tocontrol a model state. The gas turbine engine may include a control lawfor directing the actuator as a function of a model output. The gasturbine engine may include a model processor for generating the modeloutput, the model processor including an input object for processingmodel input and setting a model operating mode, a set state module forsetting dynamic states of the model processor, the dynamic states inputto an open loop model based on the model operating mode, wherein theopen loop model generates a current state model as a function of thedynamic states and the model input, wherein a constraint on the currentstate model is based a series of cycle synthesis modules, each member ofthe series of cycle synthesis modules modeling a component of a cycle ofthe gas turbine engine and comprising a series of utilities, theutilities are based on mathematical abstractions of physical propertiesassociated with the component. The series of cycle synthesis modules mayinclude a turbine module, which estimates a tip clearance between theturbine rotor ant the turbine case. The model processor may furtherinclude an estimate state module for determining an estimated state ofthe model based on a prior state model output and the current statemodel of the open loop model and an output object for processing theestimated state of the model to determine the model output.

In a refinement, the turbine module may estimate temperature and rate oftemperature change values for the turbine rotor and temperature and rateof temperature change values for the turbine case.

In a further refinement, the turbine module may estimate a radial growthof the turbine rotor based on the temperature and rate of temperaturechange values for the turbine rotor and may estimate a radial growth ofthe turbine case based on the temperature and rate of temperature changevalues for the turbine case.

In a further refinement, the turbine module may estimate the tipclearance between the turbine rotor and the turbine case based on theestimated radial growth of the turbine rotor and the estimated radialgrowth of the turbine case.

In a refinement, the control law may direct the actuator to regulate acoolant flow onto the turbine case to maintain a desired tip clearancebetween the turbine rotor and the turbine case.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary control system for afluid-based engineering system.

FIG. 2 is a block diagram of an exemplary engine parameter on-boardsynthesis (EPOS) module of the control system of FIG. 1.

FIG. 3 is a block diagram of an exemplary CAM input object of the EPOSof FIG. 2.

FIG. 4 is a block diagram of an exemplary compact aero-thermal (CAM)object of the EPOS of FIG. 2.

FIG. 5 is a block diagram of an exemplary open-loop model of the CAMobject of FIG. 4.

FIG. 6 is a block diagram of an exemplary output conditioning module ofthe EPOS of FIG. 2.

FIG. 7 is a block diagram of an exemplary control configuration formonitoring and controlling tip clearance using the control system ofFIG. 1

FIG. 8 is a flowchart representative of a method for monitoring andcontrolling tip clearance using the control configuration of FIG. 7.

FIG. 9 is a flowchart representative of machine readable instructionsthat may be executed to implement the example EPOS of FIGS. 1 and/or 2.

FIG. 10 is a flowchart representative of machine readable instructionsthat may be executed to implement the example CAM object of FIGS. 2and/or 4.

FIG. 11 is a block diagram of an example processing system that mayexecute the example machine readable instructions of FIGS. 7-8 and/orany elements of the present disclosure herein.

It should be understood that the drawings are not necessarily to scale.In certain instances, details which are not necessary for anunderstanding of this disclosure or which render other details difficultto perceive may have been omitted. It should be understood, of course,that this disclosure is not limited to the particular embodimentsillustrated herein.

DETAILED DESCRIPTION OF THE DISCLOSURE

Referring to the drawings and with specific reference to FIG. 1, acontrol system for a fluid-based engineering system in accordance withthe present disclosure is generally referred to by reference numeral100. Control demands may be generated by an operator interface 140 andmay be received by the engine parameter on board synthesis (EPOS) 110.For example, the operator interface 140 may be a real-time interfacesuch as a cockpit navigation system and/or an operator workstation.Additionally or alternatively, the operator interface 140 may includeanother, more generalized process control interface, which is suitablefor logging control commands to software control components 150,including, for example, a guidance, navigation, and control computer orautopilot system(s). Further, control demands may be generated by aninternal memory or any other internal programming operatively associatedwith software control elements 150.

The control elements 150 may include EPOS 110 and a control law 111 thatmay generate and/or process control instructions for the apparatus 130.The EPOS 110 and control law 111 may be implemented as software modulesdesigned to monitor, control, or otherwise act in associative functionwith regards to an apparatus 130. The control law 111 may obtain controlfeedbacks from the EPOS 110 and control commands from the operatorinterface 140. The control law 111 may generate control requests inengineering units to be processed by hardware control elements 120 tocontrol the apparatus 130.

Additionally, the software control components 150 may include an outputconditioner 113 and/or an input conditioner 115 to process output and/orinput data for the data's respective input/output destination. The inputto the EPOS 110, provided by the input conditioner 115, may be processedby fault detection and accommodation (FDA) logic 117 to detect rangefaults as well as in-range failures (e.g., rate-limit, cross-channelmismatch, etc.) and provide a reasonable input value along with a healthstatus indication for the input.

Further, the hardware control components 120 may convert digital datagenerated by the software control components 150 to an analog formreadable by the apparatus 130 (e.g., electrical signals), convert analogdata generated by the apparatus 130 into digital data readable softwarecomponents 150, condition such input and output data for readability,and/or control actuators 124 associated with the apparatus 130. Thedigital-to-analog convertor 122 can transform digital signals generatedby the control law 111 into actuator requests. The actuators 124 may beone or more devices which use control hardware to position variouscontrol components of the apparatus 130 in accordance with instructionsgenerated by the EPOS 110. Actuators, such as the actuators 124, may bedesigned to provide quick and accurate control of an apparatus.

Actuator sensors 125 may be included to measure various states of theactuators 124, wherein the actuator states (or positions) may be relatedto the physical configuration of the various control components of theapparatus 130. For example, fluid-based systems often include actuatorswhose linear or angular positions are sensed by actuator sensors 124,and which are related to the physical position of control surfaces orother control devices located proximate to a compressor, combustor,turbine and/or nozzle/exhaust assembly.

Further, the hardware control components 120 may include apparatussystem sensors 126. The apparatus system sensors 126 may measureoperational parameters associated with the apparatus 130. For example,fluid-based systems may include apparatus system sensors 126 thatmeasure the working fluid pressure, temperature and fluid flow atvarious axial and radial locations in the flow path. Apparatus systemsensors 126 may comprise a variety of different sensing devices,including, but not limited to, temperature sensors, flow sensors,vibration sensors, debris sensors, current sensors, voltage sensors,level sensors, altitude sensors and/or blade tip sensors. Apparatussystem sensors 126 may be positioned to measure operational parametersrelated to the function of apparatus 130, e.g., parameters related tocontrol commands submitted to EPOS 110 and control requests generated byEPOS 110 in order to direct actuators 124 to control apparatus 130.

Both the apparatus system sensors 126 and the actuator sensors 125 mayproduce electrical signals based upon a read-out result from saidsensors. The electrical signals produced by the actuator sensors 125 andthe apparatus system sensors 126 may be transmitted to ananalog-to-digital convertor 123. The analog-to-digital convertor mayconvert the electrical signals into digital signal data which may becompatible with and read by the EPOS 110 after processing by the inputconditioning module 115.

The apparatus 130 may be any fluid-based engineering system. Examplefluid-based engineering systems may include gas turbine engines foraviation and power generation, HVAC&R (heating, ventilation,air-conditioning and refrigeration), fuel cells, and other, moregeneralized fluid processing systems for hydrocarbon extraction,materials processing, and manufacture. In various embodiments, thephysical components of apparatus 130 include, but are not limited toincluding, compressors, combustors, turbines, shafts, spools, fans,blowers, heat exchangers, burners, fuel cells, electric motors andgenerators, reactor vessels, storage vessels, fluid separators, pipes,ducts, valves, mixers and other fluid processing or flow controldevices.

In some examples, the apparatus 130 may perform a thermodynamic cycle ona working fluid in order to generate rotational energy, electrical poweror reactive trust, to provide heating, ventilation, air conditioning andrefrigeration, or to perform other fluid processing functions. The rangeof available cycles includes, but is not limited to, the followingcycles and their derivatives: Otto cycles, Diesel cycles, Braytonturbine (or first Ericsson) cycles, Brayton jet (Barber/Joule) cycles,Bell-Coleman (reverse Brayton) cycles, Ericsson (Second Ericsson)cycles, Lenoir (pulse-jet) cycles, and Carnot, Stoddard and Stirlingcycles. Additionally or alternatively, apparatus 130 may perform anumber of individual thermodynamic processes for heating, cooling, flowcontrol, or for processing applications in agriculture, transportation,food and beverage production, pharmaceutical production, ormanufacturing, or for the extraction, transportation or processing of ahydrocarbon fuel. The range of available thermodynamic processesincludes, but is not limited to, adiabatic, isothermal, isobaric,isentropic, and isometric (isochoric or isovolumetric) transformations,exothermic reactions, endothermic reactions and phase changes.

In the present example, the apparatus 130 is a gas turbine engine. Assuch, the monitored aspects of the apparatus 130 may include, but arenot limited to, a compressor, combustor, turbine and/or nozzle/exhaustassembly. In an application for a gas turbine engine, the input andoutput values received/generated by the EPOS 110 may be vectorsrepresenting values for positions (i.e., nozzle areas, variable vaneangles, flow path areas, etc.), states, and actual sensed values ofparameters (i.e., spool speeds, gas path temperatures, pressuresproximate to components, flow rates proximate to components, etc.)related to the components of a gas turbine engine (i.e., a compressor,combustor, turbine and/or nozzle/exhaust assembly, etc.).

The data processed by the EPOS 110 are vectors containing parametersrelated to functions of the apparatus 130. Example input vectors for theEPOS 110 may include an external inputs vector (U_(E)) and a correctortruth vector (Y_(Ct)). U_(E) may contain values for external inputs tobe processed by the EPOS 110. U_(E) may describe the configurations,positions and states of various control elements in the apparatus 130.In a gas turbine engine, for example, individual elements of externalinputs vector U_(E) may have a set of values related to effectorposition; these effector position values may describe fuel flow rates,nozzle areas, variable vane angles, flow path orifice areas, and othercontrol element parameters. Further, U_(E) may have a set of valuesrelated to boundary conditions related to the operation of apparatus130. Some boundary conditions may be directly measured by apparatussystem sensors 126, such as fluid temperatures, pressures and flow ratesat physical boundaries of apparatus 130. In fluid-based applications,the boundary conditions may include boundary flow conditions and inletand outlet locations. Other boundary conditions specific to aircraftapplications include, but are not limited to, flight velocity, altitude,and bleed or power extractions parameters.

The corrector truth vector Y_(Ct) may contain data associated with realtime execution of the control system and describe the actual (sensed)values of the parameters related to the operation of apparatus 130. Theelements of Y_(Ct) may be based on measurements taken by the actuatorsensors 125 and/or the apparatus system sensors 126. Further, elementsof Y_(Ct) may be based on values derived from a well-understood andtrusted model of sensed parameters; for example, a flow rate model basedon a differential pressure drop across a Pitot tube or Venturi tube. Forgas turbine engines, typical Y_(Ct) vector elements include, but are notlimited to, spool speeds, gas path temperatures, and/or pressures allvalues of which may be proximate to engine components such ascompressors, combustors, and turbines. In the context of non-real timeapplications, including calibration, Y_(Ct) may correspond to highfidelity data which can either be physically tested or model-based.

Employing the compact aero-thermal model (CAM) of the presentdisclosure, FIG. 2 illustrates an embodiment of the EPOS 110 of FIG. 1in further detail. The EPOS 110 of FIG. 2 may include, but is notlimited to including a CAM input object 220, a compact aero-thermalmodel (CAM) object 230, and a CAM output object 240. The EPOS 110 mayreceive raw input data related to vectors U_(ERaw) and Y_(CtRaw).U_(ERaw) may contain values obtained from the actuator sensors 125, theapparatus system sensors 126 and/or any other associated sensors and/orinputs. Y_(CtRaw) may contain values obtained from the actuator sensors125, the apparatus system sensors 126 and/or any other associatedsensors and/or inputs.

The CAM input object 220 may package selected values from the receivedinput U_(E) vector into an input vector U_(E) _(_) _(in). Similarly, theCAM input object 220 may package selected values from the received inputinto an input corrector truth vector, Y_(Ct) _(_) _(in). Further, thevectors U_(E) _(_) _(in) and Y_(Ct) _(_) _(in) may then be conditionedto protect the input values; this may be done by range limiting thevalues, by constraining the values based on instructions, and/or byperforming any additional input modifications function on the vectors.The CAM input object 220 may also use the input vectors received todetermine an operating mode (OpMode) for the FADEC 110. The inputconditioning module 220 may output a conditioned external input vectorU_(E), a conditioned truth vector Y_(Ct), and an OpMode vector.

The input validity of the vector values may be ensured by faultdetection and accommodation logic (e.g., through processing from the FDAlogic 117) specific to each sensor input of the CAM input object 220.The fault detection and accommodation logic detects range faults as wellas in-range failures (i.e., rate-limit, cross-channel mismatch, etc.)and provides a reasonable value in all cases along with a health statusindication. An example CAM input object 220 is described in greaterdetail below with reference to FIG. 3.

The output of CAM input object 220 is received by the CAM object 230.The CAM object 230 may contain aero-thermal representations, orcomponent modules, of engine components. The component modules withinthe CAM object 230 may operate according to the system's constraintsrelated to mathematical abstractions of physical laws that governbehavior of the apparatus 130 (i.e., laws of conservation of energy,conservation of mass, conservation of momentum, Newton's 2^(nd) law forrotating systems, and/or any additional known calculable physics model).The system constraints for each contained module within the CAM object230 may have specific constraints programmed within to simulate amonitored area and/or function of the apparatus 130 (i.e., a bypass ductbleeds module, a low spool compressor module, a burner module, aparasitic power extraction module, etc.).

The CAM object 230 may use the input vectors along with internal solverstates, representing on-board corrector states, solver states, andphysics states, while functioning. The solver states may be introducedto address fast dynamics, resolve algebraic loops and smooth highlynon-linear model elements. The CAM object 230 may output a synthesizedparameters vector Y. The vector Y may be estimated in relation to theoperating range determined by the CAM object 230. An example CAM object230 is described in greater detail below with reference to FIG. 4.

The output of the CAM object 230 may be received by the CAM outputobject 240. The CAM output object 240 may post-process select CAMoutputs that are needed by consuming control software and/or hardware.For some outputs, the CAM output object 240 may perform a unitconversion, may apply a test adder, and/or may perform interpolationbetween ambient conditions and/or CAM output during starting operation.The CAM output object 240 may unpack the Y vector into values specificvalues desired by related components (i.e., temperatures, pressures,flows, sensor temperatures, and/or other output synthesis). The CAMoutput object 240 may also output inter-component station flows,temperatures, pressures, and/or fuel to air ratios, torque, thrust,bleed flows, and/or compressor and turbine case clearances. The CAMoutput object 240 may also indicate the current status (e.g., theaforementioned “OpMode” operating mode) as determined by the EPOS 110.An example CAM output object 240 is described in greater detail belowwith reference to FIG. 10.

Returning to the CAM input object 220, FIG. 3 illustrates an exemplaryembodiment of the CAM input object 220 of FIG. 2. The CAM input objectof FIG. 3 may include a U_(E) vector packager 310, a Y_(Ct) vectorpackager 320, an OpMode Determiner 330, and an input protection module340. The U_(E) vector packager may receive vector U_(ERaw) and the U_(E)vector packager 310 may select the desired values from the input vectorsand may create vector U_(E) _(_) _(in), which may be output to the inputprotection module 340. The U_(E) vector packager 310 may also be usedfor unit conversion and for synthesizing values for U_(E) which may notbe present. Similarly, the Y_(Ct) vector packager may receive vectorY_(CtRaw). The Y_(Ct) vector packager 310 may select the desired valuesfrom the input vectors and may create vector Y_(Ct) _(_) _(in), whichmay be output to the input protection module 340.

The OpMode determiner 330 may establish the operating mode of the CAMobject 230 based on the health status of input values that are necessaryto run in each operating mode. The health status may include status ofcontrol sensors determined by the FDA logic 117, as well as internallygenerated information by the CAM object 230, such as conditions ofinternal states and outputs. The OpMode determiner 330 may operate usinga logic design that seeks the highest fidelity mode based on availableinputs and may fall back to decreased fidelity modes to accommodatefaults. The operating mode determined by the OpMode determiner 330 isone of a programmed list of operating modes related to the function ofthe apparatus 130 and based on the input vector values and/or thecondition of CAM states and/or outputs. The functions of variousdownstream elements may be affected by the resulting operating modedetermined by the OpMode determiner 330.

Once the OpMode of the CAM is determined, the input protection module340 uses the OpMode vector and the input from the vector packagers 310,320 to determine input vectors U_(E) and Y_(Ct). Said vectors arereceived by the CAM object 230, illustrated in greater detail in FIG. 4.

The CAM object 230 may generate state vectors X_(C), X_(S), and X_(P).The physics state vector (X_(P)) contains simulated parameters relatedto dynamics of the apparatus 130 during a time of interested, whosederivatives (X_(pDot)) are calculated in an open loop model 410. Thevector X_(P) may include, but is not limited to including, spool shaftspeeds, apparatus material temperatures, etc. The solver state vectorX_(S) may contain values related to adjustments that are made to certaincomponents of the apparatus 130. These values may be adjustments to makeup for errors coming from the CAM. The X_(C) vector may contain valuesrelated to on-board corrector states, which are simulated componentlevel values that are a refinement of the Y_(Ct) vector to make themodeled Y_(C) vector comparable to the real values of Y_(Ct). Further,because the CAM object 230 may be implemented as a discrete real timesimulation, the CAM object 230 may fully execute each dynamic pass ofthe simulation, each pass being denoted by letter k. The product of eachsimulation pass k and simulation time step dt is the simulation time.The values of k may increase sequentially by 1, (e.g. k=[1, 2, 3, . . .]).

The set state module 420 may receive input of the U_(E), Y_(Ct), andOpMode vectors. Additionally, the set state module 420 may receive theprior state's values of the X vectors generated by the estimate statemodule 440. In FIG. 4, the prior state is denoted by the state “(k−1).”The set state module 420 may override the states in the X vectors with abase point value; however, if the values do not need an override, theset state module 420 may act as a pass through element. The overridefunctions of the set state module 420 may override the values within theX vectors with base-point (U) or external (Y_(Ct)) values depending onthe operating mode (OpMode) input to the CAM Module 230. To obtainoverride values, the set state module 420 may look up base-point valuesfor CAM states for use during initialization. The set state module 420may also select an active subset of solver states. As output, the setstate module 420 may generate corrector state (X_(C)), solver state(X_(S)), and physics state (X_(P)) vectors for use in the open loopmodel 410.

The open loop model 410 may consist of one or more cycle synthesismodules, each cycle synthesis module relating to a component, function,and/or condition associated with the apparatus 130. In the presentexample, the open loop model 410 is composed of a variety of cyclesynthesis modules that may represent components, functions and/orconditions associated with a cycle of apparatus 130. The open loopmodule is not limited to any specific number of modules and may containany number of modules that are used to simulate components, functions,and/or conditions associated with apparatus 130. The open loop model 410may receive input of the corrector state (X_(C)), solver state (X_(S)),and physics state (X_(P)) vectors from the set state module 420 and theeffector/boundary condition vector (U_(E)). The values input to the openloop model 410 may be used as input to the various modules simulatingthe components of apparatus 130. The open loop model 410 uses the valuesgenerated by the cycle synthesis modules to form a synthesizedparameters vector Y(k) based on U_(E)(k) and X(k). The synthesizedparameters vector Y(k) contains synthesized cycle values determined fromthe simulated physics of the open loop module 410 and may be used forcontrol of the apparatus 130.

Showing the series of cycle synthesis modules, FIG. 5 illustrates anexample embodiment of open loop model 410 of FIG. 4. The open loop model410 may include a group of primary stream modules 510, a group ofsecondary stream modules 520, a group of additional modules 530 and avector data packager 540. The open loop model 410 may receive input ofthe corrector state (X_(C)), solver state (X_(S)), and physics state(X_(P)) vectors, from the set state module 420, and the effector vector(U_(E)). The group of primary stream modules 510, the group of secondarystream modules 520, and the additional modules 530 all may receive inputfrom the corrector state (X_(C)), solver state (X_(S)), and physicsstate (X_(P)) vectors from the set state module 420 and the effectorvector (U_(E)). Further, the open loop module 410 is not limited toincluding the above mentioned groups of modules, but rather, the openloop module 410 may omit groups of modules and/or include other groupsof modules. Any and all modules of the open loop model 410 may interactwith one another to produce the output of each module.

Each module of the open loop module 410 may represent a component of theapparatus 130 and may be implemented by a library of utilities, whereineach utility may be a mathematical representation of the physicalproperties that make up various parts of component calculations. Forexample, utilities of the modules may include representations ofcompressors, turbines, bleeds, pressure losses, etc. that may bereusable throughout the CAM object 230 and may improve readability andmaintainability of the EPOS 110. These components may be built fromphysics representations of aerodynamic and thermodynamic processes. Eachmodule may produce, for example, an output vector containing, forexample, total pressure, total temperature, fuel/air ratio, and gas flowat the exit of the component, and/or any other parameter associated withthe modeled portion of the apparatus 130.

In some examples, the primary stream modules 510 may include, but arenot limited to including, the following based on corresponding elementsof the apparatus 130: a CMP_L module 605 modeling a low spoolcompressor, a D_BLD_STB 610 module modeling a bleed associated with alow spool compressor, a D_CS_INT module 615 modeling a pressure lossassociated with air flow passing through a duct of a compressor, a CMP_Hmodule 620 modeling a high spool compressor, a D_I030 625 modulemodeling a pressure loss associated with instrumentation in a duct atthe exit of a high spool compressor, a D_DIF_BURN module 630 modeling adiffuser, a BRN_PRI module 635 modeling a burner, a TRB_H module 640modeling a high spool turbine, a TRB_L module 645 modeling a low spoolturbine, a D_EGV_LT module 650 modeling a low turbine exit guide vaneduct, a D_I0495 module 655 modeling pressure losses associated withprobes aft of an exit guide vane duct, a D_I_NOZ_PRI module 660 modelingpressure losses related to air moving through a duct, the ductcontaining instrumentation probes, a D_TEC_NOZ module 665 modeling aprimary nozzle duct, a D_NOZ_PRI module 670 modeling pressure lossesassociated with the primary nozzle duct, and a NOZ_PRI module 675 aprimary nozzle. Additionally, the secondary stream modules 520 mayinclude, but are not limited to including, the following based oncorresponding elements of the apparatus 130: an example CMP_F_SEC module705 modeling a fan outer diameter compressor, a D_EGV_FO module 710modeling a fan exit guide vane duct, a D_BLD_SEC module 715 modeling anexit duct between a low compressor and a high compressor, a D_AVE_140module 720 modeling a duct downstream of a B25 bleed, a D_BLD_NOZ_SECmodule 725 modeling bypass duct bleeds of the apparatus 130, aD_I_NOZ_SEC module 730 modeling a secondary nozzle duct, and a NOZ_SECmodule 735 modeling a secondary nozzle. Further, there are severalmodules that are not associated with a particular stream, the modules530 may include, but are not limited to including, the following basedon conditions of the apparatus 130: a POWER_EXTRACT module 805 modelingeffects of energy and/or efficiency losses of the apparatus 130, aFAN_ID_POWER module 810 modeling an accounting for power loss from a fangear box of the apparatus 130, and a TORQUE_BALANCE module 815 modelingan accounting for conservation of energy related to unsteady torquebalance within the apparatus 130, and a CALC_ERR_SLVR module 820 thatforms solutions to errors detected in the OLM 410. Further, anyadditional modules and/or groups of modules modeling any additionalphysical properties associated with the apparatus 130 may be included aspart of the open loop model 410.

The example modules of FIG. 5 may be designed using one or more physics-based configurable utilities. The configurable utilities may becontained in a library of subsystems within the EPOS structure. The openloop module 440 may compile the above mentioned modules from thesubsystems of physics-based configurable utilities based onpreprogrammed instructions and/or a user input.

Once data is processed throughout various modules of the open loop model410, the vector data packager 540 receives input data from the group ofprimary stream modules 510, the group of secondary stream modules 520,and the additional modules 530. In addition to the synthesizedparameters vector Y(k) based on the model state and input, the open loopmodel may also output a solver errors vector (ErrSlvr) related to thesolver states vector (X_(S)). Also, the open loop model 410 may collectand output a physics state derivatives vector (X_(pDot)). The receiveddata may be packaged by the vector data packager 540 in the form ofvectors Y(k), ErrSlvr, and X_(pDot). Further, the vector data packagermay package vector data into fewer vectors and/or additional vectors.

Certain utilities used to comprise said modules of the open loop model410 may model gas-based properties and may represent, for example,specific heat as a function of temperature and fuel/air ratio, relativepressure as a function of enthalpy and fuel/air ratio, enthalpy as afunction of temperature and fuel/air ratio, specific heat ratio as afunction of temperature and fuel/air ratio, relative pressure as afunction of temperature and fuel/air ratio, relative pressure as afunction of temperature and fuel/air ratio, temperature as a function ofenthalpy and fuel/air ratio, and/or gas constant and specific heat ratioas a function of temperature and fuel/air ratio. Other example utilitiesmay model thermal conductivity as a function of gas total temperature,the absolute viscosity as a function of gas total temperature, criticalflow parameters as a function of specific heat and a gas constant,coefficients of thermal expansion as a function of material temperatureand/or type, material specific heat as a function of materialtemperature and/or type, and/or material thermal conductivity as afunction of material temperature and type. Further, other utilitiesmodeling other gas related functions may be present. Additionally oralternatively, any other utilities modeling any other propertiesassociated with the apparatus 130 may be included.

Additionally, the modules comprising the open loop model 410 may includeone or more configurable utilities. The configurable utilities may becomplex representations of engine components. For example, aconfigurable utility may represent a particular physical effect in majorengine components such as a compressor or a turbine. Each instance of aconfigurable utility may be selected to be one of severalrepresentations of the physical processes it models, even though theinterface of the configurable model remains unchanged. Configurableutilities may reconfigure themselves to represent a particular componentby switching the underlying configurable subsystems. Using suchconfigurable utilities may benefit the maintainability of a softwareapplication of the open loop model 430.

In an example embodiment, a configurable utility may be designed tomodel a Reynolds effect in the compressors of the apparatus 130 and mayreconfigure itself to represent a particular component of the compressor(e.g., a high spool compressor, a low spool compressor, etc.). Thisexample configurable, compressor Reynolds effect utility may be used information of cycle synthesis modules and may be used in modulessimulating a low spool compressor (e.g., the CMP_L module 605 of FIG.5), a high spool compressor (e.g., the CMP_H module 620 of FIG. 5),and/or any other module associated with a compressor simulation.Similarly, specific modules of the open loop model 410 may utilize aconfigurable utility to model Reynolds effect in the turbines of theapparatus 130 and may reconfigure itself to represent a particularcomponent of the compressor.

In an example OLM 410, a specific utility may model physical processesof a compressor of the apparatus 130 and may include representations ofsuch physical processes as sub-utilities. Sub-utilities of an examplecompressor utility may include, but is not limited to including, basicphysics utilities associated with the basic physics of the apparatus 130(e.g., isentropic compression, laws of thermodynamics, ideal gasproperties, etc.), a component aero-thermal map evaluation, gas-materialheat transfer properties, models of component bleeds, torque componentsfrom adiabatic steady-state, and/or effects of scaling between map andcycle conditions (design, gas property, Reynolds effect, clearance,untwisting effects, etc.). The output of such a compressor utility mayinclude, but is not limited to including, component exit gas flowconditions, bleed flows, swirl angles, total component inlet gas flows,torque extracted, and derivatives of material temperatures. A compressorutility may be operatively associated with other utilities (e.g.,off-board correction look up tables with selectable schedulingparameters, a selector for enabling on-board and/or off-board correctionfor the component, etc.) to form a compressor-related module like, forexample, the CMP_L module 605 and/or the CMP_H module 620.

Another example of an OLM model utility is the turbine utility.Sub-utilities of an example turbine utility may include, but are notlimited to including, basic physics utilities associated with the basicphysics of the apparatus 130 (e.g., isentropic expansion, laws ofthermodynamics, ideal gas properties, etc.), a component aero-thermalmap evaluation, gas-material heat transfer properties, models of inletguide vanes, models of consolidated turbine cooling bleeds, rotor inlettemperature calculation, turbine clearance effects, and/or effects ofscaling between map and cycle conditions (design, gas property, Reynoldseffect, clearance, untwisting effects, etc.). Outputs of such an exampleturbine utilities may include, but are not limited to including,component exit gas conditions, rotor inlet temperature, flows into theturbine, generated torque, material temperature derivatives, steadystate material temperature at current conditions, material temperaturetime constants, radius of apparatus from thermal expansion, and/orclearance values. A turbine utility may be operatively associated withother utilities (e.g., off-board correction look up tables withselectable scheduling parameters, a selector for enabling on-boardand/or off-board correction for the component, etc.) to form acompressor-related module like, for example, the TRB_H module 640 and/orthe TRB_L module 645.

In some examples, utilities modeling a turbine, or any other rotor-basedelement of the apparatus 110, may account for variances in tip clearancedue to changes in parameters or conditions within, for example, aturbine of the apparatus 130. Tip clearance calculations may be madewithin the TRB_H module 640 and/or any other module which models anaspect of the apparatus 130 which may be affected by a tip clearancevariance.

A turbine tip clearance is the distance between the end of a rotatingpart of a turbine rotor and an outer casing of its respectiveturbomachinery. The tip clearance experiences variance because thematerials expand and contract based on temperature changes, rotary speedchanges, gas path pressure, mechanical growth, and the like. Based onthe tip clearance distance variance, the dynamic performance of theengine (or, likewise, the estimation of the engine) is affected. If theclearance grows or shrinks, there will be changes to the flow andpressure values within the turbine. Dynamic tip clearance values may bemodeled, for example, by the TRB_H module 640 and/or any other cyclesynthesis module.

Returning to FIG. 4, the sensing synthesis module 430 may model controlsensor measurements that differ from corresponding average gas pathengine station estimates packaged in Y due to regime/location effects inthe sensor surroundings and sensor body thermal inertia. Receiving inputof Y and the derivative physics state vector (X_(pDot)), the sensingsynthesis module 430 may act as another means of fault or errordetection within the CAM module 230.

Receiving input of Y(k), Y_(C)(k), X_(pDot)(k), and/or ErrSlvr(k), theestimate state module 440 may use said inputs to determine the next passvalue of CAM state vector X(k). The estimate state module 440 may scaleand correct solver state error vector, select solver gain schedulingparameters, calculate solver state gains, calculate scale, and correctthe corrector state error vector, integrate state derivatives, applystate integrator range limits, reset state integrators duringinitialization, detect saturated state integrators, and/or detectinternal errors indicated by unreasonably large synthesized values.

The estimate state module 440 may receive input of the effector vector(U_(E)), the synthesized parameters vector (Y), the on-board correctorstate vector (X_(C)), the physics state vector (X_(P)), the solver statevector (X_(S)), the solver errors vector (errSlver), and the physicsstate derivatives vector (X_(pDot)). The estimate state module 440 mayoutput updated versions of the on-board corrector state vector (X_(C)_(_) _(ESM)), the physics state vector (X_(P) _(_) _(ESM)), and thesolver state vector (X_(S) _(_) _(ESM)). These vectors are the analyzedstate vectors from the current iteration of the open loop model 410.

The output of the estimate state module 440 is received by the CAMoutput object 240, which is illustrated in FIG. 6. The CAM output object240 may include, but is not limited to including, a vector unpacker 910,a temperatures valuator 920, a pressures valuator 930, a flows valuator940, a sensor temperatures valuator 950, an other output synthesizer960, and a status indicator 970. The vector unpacker 910 may receiveinput of the synthesized parameters vector (Y). The vector unpacker 910may output the unpacked Y vector to other elements of the outputconditioning module. The status indicator 970 may receive input of theoperating mode vector (OpMode). Further, the output conditioning module240 is not limited to including the above mentioned elements, butrather, output conditioning module 240 may omit elements and/or includeother elements.

The temperatures valuator 920 may process temperature related values ofthe synthesized parameters vector (Y). This may include performing unitconversions, implementing test adders, performing non-linearinterpolation between temperature values during the starting functionsof the apparatus 130, and/or obtaining temperature values from defaulttables as backup if/when needed. The temperatures valuator 920 is notlimited to functioning in the above mentioned manner, rather, thetemperatures valuator 920 may omit any of the listed functions and/oradd additional functions related to processing temperature data of thesynthesized parameters vector (Y).

The pressures valuator 930 may process pressure related values of thesynthesized parameters vector (Y). This may include performing unitconversions, implementing test adders, performing non-linearinterpolation between pressure values during the starting functions ofthe apparatus 130, and/or obtaining pressure values from default tablesas backup if/when needed. The pressures valuator 930 is not limited tofunctioning in the above mentioned manner, rather, the pressuresvaluator 930 may omit any of the listed functions and/or add additionalfunctions related to processing pressure data of the synthesizedparameters vector (Y).

The flows valuator 940 may process fuel flow related values of thesynthesized parameters vector (Y). This may include performing unitconversions, implementing test adders, performing non-linearinterpolation between fuel flow values during the starting functions ofthe apparatus 130, and/or obtaining fuel flow values from default tablesas backup if/when needed. The flows valuator 940 is not limited tofunctioning in the above mentioned manner, rather, the flows valuator940 may omit any of the listed functions and/or add additional functionsrelated to processing fuel flow data of the synthesized parametersvector (Y).

The sensor temperatures valuator 950 may process sensor temperaturerelated values of the synthesized parameters vector (Y). This mayinclude performing unit conversions, implementing test adders,performing non-linear interpolation between temperature values duringthe starting functions of the apparatus 130, and/or obtaining sensortemperature values from default tables as backup if/when needed. Thesensor temperatures valuator 950 is not limited to functioning in theabove mentioned manner, rather, the sensor temperatures valuator 950 mayomit any of the listed functions and/or add additional functions relatedto processing sensor temperature data of the synthesized parametersvector (Y).

The other output synthesizer 960 may process other output data of thesynthesized parameters vector (Y) not processed by the temperaturesvaluator 920, the pressures valuator 930, the flows valuator 940, thesensors and/or the temperatures valuator 950. This may includeperforming unit conversions, implementing test adders, performingnon-linear interpolation between temperature values during the startingfunctions of the apparatus 130, and/or obtaining other output valuesfrom default tables as backup if/when needed. The flows valuator 940 isnot limited to functioning in the above mentioned manner, rather, theflows valuator 940 may omit any of the listed functions and/or addadditional functions related to processing other output data of thesynthesized parameters vector (Y).

The status indicator 970 may receive input from the operating modevector (OpMode). Using this input, the status indicator 970 may generateand provide a status indication of the operating status of the CAMmodule 230 for use in any downstream logic devices.

The control system 100 may be implemented to monitor and/or control thetip clearance of a rotor element of the apparatus 130. Turbomachinerytip clearances are a contributor to component flow and capacityefficiency. For example, minimizing tip clearances in a turbine of theapparatus 130 may allow for a higher pressure ratio per stage of thecycle, may improve the stall margin of the apparatus 130, and/or maylimit the amount of backflow around the blades of the turbine rotor.Minimizing the tip clearances may allow each turbine stage to capturemore of the energy available in the flow, thus directly improving thrustspecific fuel consumption. Further, knowledge of tip clearance, by acontrol system, may be important to preventing blade-to-case rubbingwhile still optimizing performance and operability of the cycle of theapparatus 130.

Using the CAM based EPOS 110, expansion and contraction of materialswithin the apparatus 130 can be modeled in the OLM 410. The OLM 410 mayestimate the temperature and change in temperature values associatedwith rotor and rotor case materials of rotor-based components of theapparatus 130. The temperature and rate of change of temperature of therotor and rotor case components may be used to estimate expansion andcontraction of the rotor and rotor case components. Theexpansion/contraction properties of the rotor and case may be calculatedbased on the heat's effect on the original mass/density of the rotorand/or case. Therefore, once the expansion/contraction values are knownfor the rotor and rotor case, the tip clearance between them within therotor element of the apparatus 130 can be estimated as the tip clearancedistance (CLR) and the rate of change in tip clearance (CLR_(SS)). Otherparameters associate with tip clearance may be used in calculation ofthe CLR and/or CLR_(SS). Additionally or alternatively, the OLM 410 maymonitor radial expansion by monitoring rotary speed, gas path pressures,and associated thermal and/or mechanical growth.

Turning now to FIG. 7, a control configuration 160 for determining andcontrolling tip clearance in a rotor based element of the apparatus 130is shown. As described in further detail above, the CAM-based EPOS 110receives effector positions and boundary conditions (U_(fb)) from theapparatus 130 via the hardware control components 120. Additionally, theEPOS 110 may receive engine/cycle measurements (Y_(m)) from theapparatus 130, via one or more sensors and/or other hardware input.Using the input, the EPOS 110 generates a model output for control.

In particular, the EPOS 110 may determine a value for the tip clearance(CLR) and rate of change in tip clearance (CLR_(SS)) for a given rotorbased element of the apparatus 130 using the model output. The rotorbased element may be, for example, a turbine associated withturbomachinery, the turbine having a rotor and a case. The softwarecontrol elements 150 may also determine goal values for the tipclearance (CLR_(goal)) and rate of change in tip clearance (CLR_(SS)_(_) _(goal)). The goal values may be produced by the EPOS 110 and/orthe goal values may be determined using input generated from theoperator interface 140.

The generated values of CLR, CLR_(SS), CLR_(goal), and CLR_(SSgoal) maybe used to determine advanced multi-variable control (AMVC) instructions165 for output to the control hardware. The AMVC 165 may be executed bythe control law 111. In some examples, control error (errCtrl)processing may be applied to the input of the AMVC 165. The AMVCinstructions 165 are used by hardware control elements 150, such as theactuators 124), to generate a control request (U_(fb)) for the apparatus150. Using the control configuration 160, the control requests may beused to control and monitor tip clearances as described in the flowchart980 of FIG. 8.

At block 982, the model output of the EPOS 110 may generate an estimatedmaterial temperature and/or an estimated material temperature rate ofchange (both values contained in the X_(f) vector) for a rotor and arotor case of a rotary component of the apparatus 130.). The radialgrowth of the rotor and the rotor case may be determined based on thetemperature values and using thermodynamic laws to estimate expansionand/or contraction (block 984). Using the radial growth values, the tipclearance between the rotor and the rotor case can be determined (block986). Additional parameters may be used when calculating tip clearance.

The tip clearance values may be determined during computations by theopen loop model 410, wherein specific cycle synthesis modules pertainingto rotary elements may produce the values. The cycle synthesis modulesmay employ a utility from the series of utilities (e.g., athermodynamics law utility) which determines temperatures and/ortemperature rates of change for a material of a rotary element modeledby its respective cycle synthesis module.

Continuing to block 988, the control law may receive a tip clearancesignal; the tip clearance signal may include the value for CLR. Usingthe tip clearance signal, the control law 111 may determine controls forthe flow along the gas path of the gas turbine engine at a specifiedelement based on tip clearance signal. For example, the control law 111may direct the actuators 124 to regulate a coolant flow onto the rotorcase to maintain a desired tip clearance between the rotor and the rotorcase. Determinations for the controls output by the control law 111 maybe based on goal values for tip clearance. Additionally oralternatively, the controls output may be based on other preprogrammingand/or user input.

While an example manner of implementing the EPOS 110 of FIG. 1 has beenillustrated in FIGS. 2-6, one or more elements, processes, and/ordevices illustrated in FIGS. 2-6 may be combined, divided, rearranged,omitted, eliminated and/or implemented in any other way. Further, theexample elements of FIGS. 1-7 could be implemented by one or morecircuit(s), programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When any of the apparatusor system claims of this patent are read to cover a purely softwareand/or firmware implementation, at least one of the example elements arehereby expressly defined to include a tangible computer readable mediumsuch as a memory, DVD, CD, Blu-ray, etc. storing the software and/orfirmware. Further still, the example embodiments of have beenillustrated in may include one or more elements, processes and/ordevices in addition to, or instead of, those illustrated in FIGS. 1-7,and/or may include more than one of any or all of the illustratedelements, processes and devices.

Flowcharts representative of example machine readable instructions areshown in FIGS. 9 and 10. In these examples, the machine readableinstructions comprise a program for execution by a processor such as theprocessor such as the processor 1210 shown in the example computer 1200discussed below in connection with FIG. 11. The program may be embodiedin software stored on a tangible computer readable medium such as aCD-ROM, a floppy disk, a hard drive, a digital versatile disk (DVD), aBlu-ray disk, or a memory associated with the processor 1210, but theentire program and/or parts thereof could alternatively be executed by adevice other than the processor 1210 and/or embodied in firmware ordedicated hardware. Further, although the example programs are describedwith reference to the flowcharts illustrated in FIGS. 9 and 10, manyother methods of implementing embodiments of the present disclosure mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined.

With reference to FIG. 9, example machine readable instructions 1000 maybe executed to implement the EPOS 110 of FIGS. 1 and/or 2. Withreference to FIGS. 1 and/or 2, the example machine readable instructions1000 begin execution at block 1010 at input vectors are received by theCAM input object 220 and are used by the CAM input object 220 todetermine the operating mode (OpMode) of the simulation and to compilethe CAM input vectors U_(E) and Y_(Ct) (block 1015). The CAM inputvectors are then used by the CAM object 230 to determine synthesizedparameters vector Y based on internal physics state module of the CAMmodule 230 and the external inputs U_(E), Y_(Ct), and OpMode (block1020). The synthesized parameters vector Y is conditioned by the CAMoutput object 240 for use by external modules such as, for example, thecontrol law 123 of FIG. 1 (block 1025).

The example machine readable instructions 1100 of FIG. 10 may beexecuted to implement the CAM object 230 of FIGS. 2 and/or 4. Withreference to FIGS. 2 and/or 4, the set state module receives input fromand sets the state of the CAM object 230 based upon vectors U_(E),Y_(C)(k), OpMode, and prior physics state vectors generated by theestimate state module 440 in the form of X_(E) _(_) _(ESM)(k−1), X_(C)_(_) _(ESM)(k−1), and X_(P) _(_) _(ESM)(k−1) (block 1110). The open loopmodel 410 determines synthesized parameters vector Y(k) by processingdata contained in U_(E), Y_(C)(k), X_(C)(k−1), X_(S)(k−1), andX_(P)(k−1) using one or more contained cycle synthesis modules, the oneor more cycle synthesis modules being a mathematical abstraction(s) ofthe physics states associated with an element of a cycle of theapparatus 130 (block 1115). The sensing synthesis module 430 receivesthe synthesized parameters vector Y(k) and detects potential errors inthe vector (block 1120). The estimate state module 440 determines thephysics state vectors X_(S) _(_) _(ESM)(k), X_(C) _(_) _(ESM)(k), andX_(P) _(_) _(ESM)(k) for the present state (k) (block 1125). Theestimate state module 440 outputs the vectors X_(S) _(_) _(ESM)(k),X_(C) _(_) _(ESM)(k), and X_(P) _(_) _(ESM)(k) to the set state module420 for processing the next sequential state (block 1130). The estimatestate module 440 outputs the vector Y(k) for use external to the CAMmodule 230 (block 1135).

FIG. 11 is a block diagram of an example computer 1200 capable ofexecuting the instructions of FIGS. 8-9 to implement the apparatus ofFIGS. 1-7. The computer 1200 can be, for example, a server, a personalcomputer, or any other type of computing device.

The system 1200 of the instant example includes a processor 1210. Forexample, the processor 1210 can be implemented by one or moremicroprocessors or controllers from any desired family or manufacturer.

The processor 1210 includes a local memory 1215 and is in communicationwith a main memory including a read only memory 1230 and a random accessmemory 1220 via a bus 1240. The random access memory 1220 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRM)and/or any other type of random access memory device. The read onlymemory 1230 may be implemented by a hard drive, flash memory and/or anyother desired type of memory device.

The computer 1200 also includes an interface circuit 1250. The interfacecircuit 1230 may be implemented by any type of interface standard, suchas an Ethernet interface, a universal serial bus (USB), and/or a PCIexpress interface.

One or more input devices 1254 are connected to the interface circuit1250. The input device(s) 1254 permit a user to enter data and commandsinto the processor 1210. The input device(s) can be implemented by, forexample, a keyboard, a mouse, a touchscreen, a track-pad, a trackball,isopoint and/or a voice recognition system. The interface 1250 mayoperate in conjunction with, in parallel with, or in place of, theoperator interface 115 of FIG. 1.

One or more output devices 1258 are also connected to the interfacecircuit 1250. The output devices 1258 can be implemented by, forexample, display devices for associated data (e.g., a liquid crystaldisplay, a cathode ray tube display (CRT), etc.), and/or an actuatoroperatively associated with a fluid-based engineering system such as gasturbine engines for aviation and power generation, HVAC&R (heating,ventilation, air-conditioning and refrigeration), fuel cells, and other,more generalized fluid processing systems for hydrocarbon extraction,materials processing, and manufacture.

INDUSTRIAL APPLICABILITY

From the foregoing, it can be seen that the technology disclosed hereinhas industrial applicability in a variety of settings such as, but notlimited to, systems and methods for controlling a fluid basedengineering system. Example fluid-based engineering systems may includegas turbine engines for aviation and power generation, HVAC&R (heating,ventilation, air-conditioning and refrigeration), fuel cells, and other,more generalized fluid processing systems for hydrocarbon extraction,materials processing, and manufacture. Using the teachings of thepresent disclosure, compact aero-thermal models of fluid-basedengineering systems may be designed to reduce computational load on asystem's control device and/or on board processor. The efficiency ofsuch a model may be improved through the use of a series of utilities,the utilities based on mathematical abstractions of physical propertiesassociated with components of the engineering system. This improvementover the prior art may conserve computational efficiency and may improvethe accuracy of the control system for fluid based engineering systems.

While the present disclosure has been in reference to a gas turbineengine of an aircraft, one skilled in the art will understand that theteachings herein can be used in other applications as well, as mentionedabove. It is therefore intended that the scope of the invention not belimited by the embodiments presented herein as the best mode forcarrying out the invention, but that the invention will include allequivalents falling within the spirit and scope of the claims as well.

What is claimed is:
 1. A control system, comprising: an actuator forpositioning a control device, the control device comprising a rotaryapparatus including a rotor and a rotor case, wherein the actuatorpositions the control device; a control law configured to direct theactuator as a function of a model output; and a model processorconfigured to generate the model output, the model processor comprising:an input object for processing a model input vector and setting a modeloperating mode; a set state module for setting dynamic states of themodel processor, the dynamic states input to an open loop model based onthe model operating mode; wherein the open loop model generates currentstate derivatives, solver state errors, and synthesized parameters as afunction of the dynamic states and the model input vector, wherein theopen loop model constrains the current state derivatives and solverstate errors based on a series of cycle synthesis modules, each memberof the series of cycle synthesis modules modeling a component of a cycleof the control device and comprising a series of utilities, theutilities based on mathematical abstractions of physical laws thatgovern behavior of the component, the series of cycle synthesis modulesincluding a rotary apparatus module which estimates a tip clearancebetween the rotor and the rotor case; an estimate state moduleconfigured to determine an estimated state of the model based on atleast one of a prior state, the current state derivatives, the solverstate errors, and the synthesized parameters; and an output object forprocessing at least the synthesized parameters of the model to determinethe model output.
 2. The control system of claim 1, wherein the rotaryapparatus module estimates temperature and rate of temperature changevalues for the rotor and temperature and rate of temperature changevalues for the rotor case.
 3. The control system of claim 2, wherein therotary apparatus module estimates a radial growth of the rotor based onthe temperature and rate of temperature change values for the rotor andestimates a radial growth of the rotor case based on the temperature andrate of temperature change values for the rotor case.
 4. The controlsystem of claim 3, wherein the rotary apparatus module estimates the tipclearance between the rotor and the rotor case based on the estimatedradial growth of the rotor and the estimated radial growth of the rotorcase.
 5. The control system of claim 1, wherein the control law directsthe actuators to control a flow in a gas path of the rotary apparatusbased on the tip clearance.
 6. The control system of claim 5, whereinthe control law directs the actuator to regulate a coolant flow onto therotor case to maintain a desired tip clearance between the rotor and therotor case.
 7. The control system of claim 1, wherein the rotaryapparatus includes at least one of a compressor element or a turbineelement.
 8. The control system of claim 1, wherein the estimated tipclearance is based on one or more of material temperature estimations,rotary speed calculations, gas path pressure estimations, and mechanicalgrowth estimations.
 9. The control system of claim 1, wherein thecontrol device is a gas turbine engine.
 10. A method for controlling acontrol device, the control device comprising a rotary apparatusincluding a rotor and a rotor case, the method comprising: generating amodel output using a model processor; processing a model input vectorand setting a model operating mode; setting dynamic states of the modelprocessor, the dynamic states input to an open loop model based on themodel operating mode; generating current state derivatives, solver stateerrors, and synthesized parameters as a function of the dynamic statesand the model input vector by an open loop model that constrains thecurrent state derivatives and solver state errors based on a series ofcycle synthesis modules, each member of the series of cycle synthesismodules modeling a component of a cycle of the control device andcomprising a series of utilities, the utilities based on mathematicalabstractions of physical laws that govern behavior of the component, theseries of cycle synthesis modules including a rotary apparatus modulewhich estimates a tip clearance between the rotor and the rotor case;determining an estimated state of the model based on at least one of aprior state, the current state derivatives, the solver state errors, andthe synthesized parameters; processing at least the synthesizedparameters of the model to determine the model output; directing anactuator associated with the control device as a function of a modeloutput using a control law; and positioning a control device comprisinga control surface using the actuator, wherein the actuator positions thecontrol surface.
 11. The method claim 10, wherein the rotary apparatusmodule estimates temperature and rate of temperature change values forthe rotor and temperature and rate of temperature change values for therotor case.
 12. The method of claim 11, wherein the rotary apparatusmodule estimates a radial growth of the rotor based on the temperatureand rate of temperature change values for the rotor and estimates aradial growth of the rotor case based on the temperature and rate oftemperature change values for the rotor case.
 13. The method of claim12, wherein the rotary apparatus module estimates the tip clearancebetween the rotor and the rotor case based on the radial growth of therotor and the radial growth of the rotor case.
 14. The method of claim10, wherein the control device is a gas turbine engine and the one ormore cycle synthesis modules are based on one or more mathematicalabstractions of physical laws that govern behavior of components of athermodynamic cycle of the gas turbine engine.
 15. The method of claim10, further comprising directing the actuator, using the control law, toregulate a coolant flow onto the rotor case to maintain a desired tipclearance between the rotor and the rotor case based on the estimatedtip clearance.
 16. A gas turbine engine comprising: an actuator forpositioning the gas turbine engine comprising a control surface, whereinthe actuator positions a control surface of an element of the gasturbine engine; a control law configured to direct the actuator as afunction of a model output; a model processor configured to generate themodel output, the model processor comprising: an input object forprocessing a model input vector and setting a model operating mode; aset state module for setting dynamic states of the model processor, thedynamic states input to an open loop model based on the model operatingmode; wherein the open loop model generates current state derivatives,solver state errors, and synthesized parameters as a function of thedynamic states and the model input vector, wherein the open loop modelconstrains the current state derivatives and solver state errors basedon a series of cycle synthesis modules, each member of the series ofcycle synthesis modules modeling a component of a cycle of the gasturbine engine and comprising a series of utilities, the utilities basedon mathematical abstractions of physical laws that govern behavior ofthe component, the series of cycle synthesis modules including a turbinemodule, which estimates a tip clearance between a turbine rotor and aturbine case of the gas turbine engine; an estimate state moduleconfigured to determine an estimated state of the model based on atleast one of a prior state, the current state derivatives, the solverstate errors, and the synthesized parameters; and an output object forprocessing at least the synthesized parameters of the model to determinethe model output.
 17. The gas turbine engine of claim 16, wherein theturbine module estimates temperature and rate of temperature changevalues for the turbine rotor and temperature and rate of temperaturechange values for the turbine case.
 18. The gas turbine engine of claim17, wherein the turbine module estimates a radial growth of the turbinerotor based on the temperature and rate of temperature change values forthe turbine rotor and estimates a radial growth of the turbine casebased on the temperature and rate of temperature change values for theturbine case.
 19. The gas turbine engine of claim 18, wherein theturbine module estimates the tip clearance between the turbine rotor andthe turbine case based on a mass of the rotor and a mass of the rotorcase.
 20. The gas turbine engine of claim 16, wherein the control lawdirects the actuator to regulate a coolant flow onto the turbine case tomaintain a desired tip clearance between the turbine rotor and theturbine case.